






import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


A_						:						Optional[Any]						      =					get_tests_dir('fixtures/test_sentencepiece_no_bos.model')




@require_sentencepiece
@require_tokenizers
class 							_lowerCAmelCase(			UpperCAmelCase_				,      unittest.TestCase   ):





       """simple docstring"""







       a							:						str										=PegasusTokenizer
       a							:						List[Any]										=PegasusTokenizerFast
       a							:						Optional[Any]										=True
       a							:						List[Any]										=True





       def      _a       (   self				):
         super().setUp()

         # We have a SentencePiece fixture for testing
         UpperCamelCase_:   List[Any]        =							PegasusTokenizer(_lowerCamelCase				)
         tokenizer.save_pretrained(self.tmpdirname				)





       @cached_property
       def      _a       (   self				):
         return PegasusTokenizer.from_pretrained('google/pegasus-large'				)





       def      _a       (   self    ,     **_lowerCamelCase				):
         return PegasusTokenizer.from_pretrained(self.tmpdirname    ,     **_lowerCamelCase				)





       def      _a       (   self    ,     _lowerCamelCase				):
         return ("This is a test", "This is a test")





       def      _a       (   self				):
         UpperCamelCase_:   Optional[Any]        =							'</s>'
         UpperCamelCase_:   Dict        =							1

         self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowerCamelCase				)    ,     _lowerCamelCase				)
         self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowerCamelCase				)    ,     _lowerCamelCase				)





       def      _a       (   self				):
         UpperCamelCase_:   Optional[int]        =							list(self.get_tokenizer().get_vocab().keys()				)

         self.assertEqual(vocab_keys[0]    ,     '<pad>'				)
         self.assertEqual(vocab_keys[1]    ,     '</s>'				)
         self.assertEqual(vocab_keys[-1]    ,     'v'				)
         self.assertEqual(len(_lowerCamelCase				)    ,     1_1_0_3				)





       def      _a       (   self				):
         self.assertEqual(self.get_tokenizer().vocab_size    ,     1_1_0_3				)





       def      _a       (   self				):
         UpperCamelCase_:   List[str]        =							self.rust_tokenizer_class.from_pretrained(self.tmpdirname				)
         UpperCamelCase_:   Dict        =							self.tokenizer_class.from_pretrained(self.tmpdirname				)
         UpperCamelCase_:   int        =							(
             'Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important'
             ' </s> <pad> <pad> <pad>'
         )
         UpperCamelCase_:   Any        =							rust_tokenizer([raw_input_str]    ,     return_tensors=_lowerCamelCase    ,     add_special_tokens=_lowerCamelCase				).input_ids[0]
         UpperCamelCase_:   int        =							py_tokenizer([raw_input_str]    ,     return_tensors=_lowerCamelCase    ,     add_special_tokens=_lowerCamelCase				).input_ids[0]
         self.assertListEqual(_lowerCamelCase    ,     _lowerCamelCase				)





       def      _a       (   self				):
         UpperCamelCase_:   Dict        =							self._large_tokenizer
         # <mask_1> masks whole sentence while <mask_2> masks single word
         UpperCamelCase_:   int        =							'<mask_1> To ensure a <mask_2> flow of bank resolutions.'
         UpperCamelCase_:   Optional[int]        =							[2, 4_1_3, 6_1_5, 1_1_4, 3, 1_9_7_1, 1_1_3, 1_6_7_9, 1_0_7_1_0, 1_0_7, 1]
         UpperCamelCase_:   Dict        =							tokenizer([raw_input_str]    ,     return_tensors=_lowerCamelCase				).input_ids[0]
         self.assertListEqual(_lowerCamelCase    ,     _lowerCamelCase				)





       def      _a       (   self				):
         UpperCamelCase_:   List[Any]        =							self._large_tokenizer
         # The tracebacks for the following asserts are **better** without messages or self.assertEqual
         assert tokenizer.vocab_size == 9_6_1_0_3
         assert tokenizer.pad_token_id == 0
         assert tokenizer.eos_token_id == 1
         assert tokenizer.offset == 1_0_3
         assert tokenizer.unk_token_id == tokenizer.offset + 2 == 1_0_5
         assert tokenizer.unk_token == "<unk>"
         assert tokenizer.model_max_length == 1_0_2_4
         UpperCamelCase_:   Union[str, Any]        =							'To ensure a smooth flow of bank resolutions.'
         UpperCamelCase_:   List[Any]        =							[4_1_3, 6_1_5, 1_1_4, 2_2_9_1, 1_9_7_1, 1_1_3, 1_6_7_9, 1_0_7_1_0, 1_0_7, 1]
         UpperCamelCase_:   Optional[Any]        =							tokenizer([raw_input_str]    ,     return_tensors=_lowerCamelCase				).input_ids[0]
         self.assertListEqual(_lowerCamelCase    ,     _lowerCamelCase				)
         assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]				) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]





       @require_torch
       def      _a       (   self				):
         UpperCamelCase_:   Optional[int]        =							['This is going to be way too long.' * 1_5_0, 'short example']
         UpperCamelCase_:   Optional[Any]        =							['not super long but more than 5 tokens', 'tiny']
         UpperCamelCase_:   Union[str, Any]        =							self._large_tokenizer(_lowerCamelCase    ,     padding=_lowerCamelCase    ,     truncation=_lowerCamelCase    ,     return_tensors='pt'				)
         UpperCamelCase_:   int        =							self._large_tokenizer(
             text_target=_lowerCamelCase    ,     max_length=5    ,     padding=_lowerCamelCase    ,     truncation=_lowerCamelCase    ,     return_tensors='pt'				)

         assert batch.input_ids.shape == (2, 1_0_2_4)
         assert batch.attention_mask.shape == (2, 1_0_2_4)
         assert targets["input_ids"].shape == (2, 5)
         assert len(_lowerCamelCase				) == 2  # input_ids, attention_mask.





       @slow
       def      _a       (   self				):
         # fmt: off
         UpperCamelCase_:   Optional[Any]        =							{'input_ids': [[3_8_9_7_9, 1_4_3, 1_8_4_8_5, 6_0_6, 1_3_0, 2_6_6_6_9, 8_7_6_8_6, 1_2_1, 5_4_1_8_9, 1_1_2_9, 1_1_1, 2_6_6_6_9, 8_7_6_8_6, 1_2_1, 9_1_1_4, 1_4_7_8_7, 1_2_1, 1_3_2_4_9, 1_5_8, 5_9_2, 9_5_6, 1_2_1, 1_4_6_2_1, 3_1_5_7_6, 1_4_3, 6_2_6_1_3, 1_0_8, 9_6_8_8, 9_3_0, 4_3_4_3_0, 1_1_5_6_2, 6_2_6_1_3, 3_0_4, 1_0_8, 1_1_4_4_3, 8_9_7, 1_0_8, 9_3_1_4, 1_7_4_1_5, 6_3_3_9_9, 1_0_8, 1_1_4_4_3, 7_6_1_4, 1_8_3_1_6, 1_1_8, 4_2_8_4, 7_1_4_8, 1_2_4_3_0, 1_4_3, 1_4_0_0, 2_5_7_0_3, 1_5_8, 1_1_1, 4_2_8_4, 7_1_4_8, 1_1_7_7_2, 1_4_3, 2_1_2_9_7, 1_0_6_4, 1_5_8, 1_2_2, 2_0_4, 3_5_0_6, 1_7_5_4, 1_1_3_3, 1_4_7_8_7, 1_5_8_1, 1_1_5, 3_3_2_2_4, 4_4_8_2, 1_1_1, 1_3_5_5, 1_1_0, 2_9_1_7_3, 3_1_7, 5_0_8_3_3, 1_0_8, 2_0_1_4_7, 9_4_6_6_5, 1_1_1, 7_7_1_9_8, 1_0_7, 1], [1_1_0, 6_2_6_1_3, 1_1_7, 6_3_8, 1_1_2, 1_1_3_3, 1_2_1, 2_0_0_9_8, 1_3_5_5, 7_9_0_5_0, 1_3_8_7_2, 1_3_5, 1_5_9_6, 5_3_5_4_1, 1_3_5_2, 1_4_1, 1_3_0_3_9, 5_5_4_2, 1_2_4, 3_0_2, 5_1_8, 1_1_1, 2_6_8, 2_9_5_6, 1_1_5, 1_4_9, 4_4_2_7, 1_0_7, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_3_9, 1_2_3_5, 2_7_9_9, 1_8_2_8_9, 1_7_7_8_0, 2_0_4, 1_0_9, 9_4_7_4, 1_2_9_6, 1_0_7, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
         # fmt: on

         self.tokenizer_integration_test_util(
             expected_encoding=_lowerCamelCase    ,     model_name='google/bigbird-pegasus-large-arxiv'    ,     revision='ba85d0851d708441f91440d509690f1ab6353415'    ,     )







@require_sentencepiece
@require_tokenizers
class 							_lowerCAmelCase(			UpperCAmelCase_				,      unittest.TestCase   ):





       """simple docstring"""







       a							:						Optional[int]										=PegasusTokenizer
       a							:						int										=PegasusTokenizerFast
       a							:						List[str]										=True
       a							:						List[Any]										=True





       def      _a       (   self				):
         super().setUp()

         # We have a SentencePiece fixture for testing
         UpperCamelCase_:   Optional[int]        =							PegasusTokenizer(_lowerCamelCase    ,     offset=0    ,     mask_token_sent=_lowerCamelCase    ,     mask_token='[MASK]'				)
         tokenizer.save_pretrained(self.tmpdirname				)





       @cached_property
       def      _a       (   self				):
         return PegasusTokenizer.from_pretrained('google/bigbird-pegasus-large-arxiv'				)





       def      _a       (   self    ,     **_lowerCamelCase				):
         return PegasusTokenizer.from_pretrained(self.tmpdirname    ,     **_lowerCamelCase				)





       def      _a       (   self    ,     _lowerCamelCase				):
         return ("This is a test", "This is a test")





       def      _a       (   self				):
         UpperCamelCase_:   List[str]        =							self.rust_tokenizer_class.from_pretrained(self.tmpdirname				)
         UpperCamelCase_:   Any        =							self.tokenizer_class.from_pretrained(self.tmpdirname				)
         UpperCamelCase_:   Tuple        =							(
             'Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'
             ' <pad> <pad> <pad>'
         )
         UpperCamelCase_:   str        =							rust_tokenizer([raw_input_str]    ,     return_tensors=_lowerCamelCase    ,     add_special_tokens=_lowerCamelCase				).input_ids[0]
         UpperCamelCase_:   Dict        =							py_tokenizer([raw_input_str]    ,     return_tensors=_lowerCamelCase    ,     add_special_tokens=_lowerCamelCase				).input_ids[0]
         self.assertListEqual(_lowerCamelCase    ,     _lowerCamelCase				)





       @require_torch
       def      _a       (   self				):
         UpperCamelCase_:   List[str]        =							['This is going to be way too long.' * 1_0_0_0, 'short example']
         UpperCamelCase_:   Optional[Any]        =							['not super long but more than 5 tokens', 'tiny']
         UpperCamelCase_:   Dict        =							self._large_tokenizer(_lowerCamelCase    ,     padding=_lowerCamelCase    ,     truncation=_lowerCamelCase    ,     return_tensors='pt'				)
         UpperCamelCase_:   Optional[int]        =							self._large_tokenizer(
             text_target=_lowerCamelCase    ,     max_length=5    ,     padding=_lowerCamelCase    ,     truncation=_lowerCamelCase    ,     return_tensors='pt'				)

         assert batch.input_ids.shape == (2, 4_0_9_6)
         assert batch.attention_mask.shape == (2, 4_0_9_6)
         assert targets["input_ids"].shape == (2, 5)
         assert len(_lowerCamelCase				) == 2  # input_ids, attention_mask.





       def      _a       (   self				):
         UpperCamelCase_:   Optional[Any]        =							(
             'This is an example string that is used to test the original TF implementation against the HF'
             ' implementation'
         )

         UpperCamelCase_:   int        =							self._large_tokenizer(_lowerCamelCase				).input_ids

         self.assertListEqual(
             _lowerCamelCase    ,     [1_8_2, 1_1_7, 1_4_2, 5_8_7, 4_2_1_1, 1_2_0, 1_1_7, 2_6_3, 1_1_2, 8_0_4, 1_0_9, 8_5_6, 2_5_0_1_6, 3_1_3_7, 4_6_4, 1_0_9, 2_6_9_5_5, 3_1_3_7, 1]    ,     )




'''simple docstring'''

import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch






def 				a  (     UpperCamelCase_    :    Any			)       ->	List[str]:
		snake_case__	     =os.path.join(args.tf_model_dir      ,					'parameters.json'			)
		snake_case__	     =json.loads(open(UpperCamelCase_			).read()			)
		if not params:
				raise ValueError(
				    f"""It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file."""			)
		if not args.output.endswith('.pt'			):
				snake_case__	     =args.output + '.pt'
		snake_case__	     =OrderedDict()
		with tf.device('/CPU:0'			):
				snake_case__	     =tf.train.load_checkpoint(args.tf_model_dir			)
				snake_case__	     =reader.get_variable_to_shape_map()
				for key_name in shapes.keys():
						snake_case__	     =reader.get_tensor(UpperCamelCase_			).astype(np.floataa			)
						if key_name.endswith('/adam_m'			) or key_name.endswith('/adam_v'			):
								continue
						if key_name.startswith('pasts/'			):
								if key_name.startswith('pasts/mlp'			):
										snake_case__	     =int(key_name[9]			)
								elif key_name.startswith('pasts/out'			):
										snake_case__	     =8
								snake_case__	     ='model.sqout.%d.weight' % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
								snake_case__	     =vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
								snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name.startswith('model/moe'			):
								snake_case__	     =int(key_name[9:].split('/'			)[0]			)
								if key_name.endswith('/switch_gating/kernel'			):
										snake_case__	     ='model.blocks.%d.feed_forward.mlp.router.classifier.weight' % player
										snake_case__	     =vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/softmlp/kernel'			):
										snake_case__	     ='model.blocks.%d.feed_forward.soft_bypass_mlp.weight' % player
										snake_case__	     =vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/wo/kernel'			) or key_name.endswith('/wi/kernel'			):
										snake_case__	     =key_name[-9:-7]
										for i in range(16			):
												snake_case__	     ='model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight' % (player, i, nlayer)
												snake_case__	     =(
												    vnp[i].transpose([1, 0]			).copy()
												)  # In Mesh-Tensorflow, it is one array, so it is divided
												snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name.startswith('model/mlp'			):
								snake_case__	     =int(key_name[9:].split('/'			)[0]			)
								if key_name.endswith('/p1/kernel'			):
										snake_case__	     ='model.blocks.%d.feed_forward.mlp.wi.weight' % player
										snake_case__	     =vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/p1/bias'			):
										snake_case__	     ='model.blocks.%d.feed_forward.mlp.wi.bias' % player
										snake_case__	     =vnp.copy()  # same because it is one dimensional
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/p2/kernel'			):
										snake_case__	     ='model.blocks.%d.feed_forward.mlp.wo.weight' % player
										snake_case__	     =vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/p2/bias'			):
										snake_case__	     ='model.blocks.%d.feed_forward.mlp.wo.bias' % player
										snake_case__	     =vnp.copy()  # same because it is one dimensional
										snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name.startswith('model/ln'			):
								snake_case__	     =int(key_name[8:].split('/'			)[0]			)
								if key_name.endswith('/b'			):
										snake_case__	     ='model.blocks.%d.feed_forward.norm.bias' % player
										snake_case__	     =vnp.copy()  # same because it is one dimensional
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/g'			):
										snake_case__	     ='model.blocks.%d.feed_forward.norm.weight' % player
										snake_case__	     =vnp.copy()  # same because it is one dimensional
										snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name.startswith('model/att'			):
								snake_case__	     =int(key_name[9:].split('/'			)[0]			)
								if key_name.endswith('/qkv/kernel'			):
										snake_case__	     =vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
										snake_case__	     =state[:, 0, :, :]
										snake_case__	     =state[:, 1, :, :]
										snake_case__	     =state[:, 2, :, :]
										snake_case__	     =(
										    state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]			)
										    .transpose([1, 0]			)
										    .copy()
										)  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     =(
										    state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]			)
										    .transpose([1, 0]			)
										    .copy()
										)  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     =(
										    state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]			)
										    .transpose([1, 0]			)
										    .copy()
										)  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     ='model.blocks.%d.self_attn.self_attn.q_proj.weight' % player
										snake_case__	     =torch.tensor(UpperCamelCase_			)
										snake_case__	     ='model.blocks.%d.self_attn.self_attn.k_proj.weight' % player
										snake_case__	     =torch.tensor(UpperCamelCase_			)
										snake_case__	     ='model.blocks.%d.self_attn.self_attn.v_proj.weight' % player
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/o/kernel'			):
										snake_case__	     ='model.blocks.%d.self_attn.self_attn.out_proj.weight' % player
										snake_case__	     =(
										    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]			).transpose([1, 0]			).copy()
										)  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name.startswith('model/an'			):
								snake_case__	     =int(key_name[8:].split('/'			)[0]			)
								if key_name.endswith('/b'			):
										snake_case__	     ='model.blocks.%d.self_attn.norm.bias' % player
										snake_case__	     =vnp.copy()  # same because it is one dimensional
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/g'			):
										snake_case__	     ='model.blocks.%d.self_attn.norm.weight' % player
										snake_case__	     =vnp.copy()  # same because it is one dimensional
										snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif (
						    key_name.startswith('model/wte'			)
						    or key_name.startswith('model/wpe'			)
						    or key_name.startswith('model/ete'			)
						):
								snake_case__	     ={'wte': 'embed_tokens', 'wpe': 'position_embeddings', 'ete': 'extra_position_embeddings'}[
								    key_name[-3:]
								]
								snake_case__	     ='model.%s.weight' % nlayer
								snake_case__	     =vnp.copy()  # same in embedded
								snake_case__	     =torch.tensor(UpperCamelCase_			)
								if key_name.startswith('model/wte'			):
										snake_case__	     ='lm_head.weight'
										snake_case__	     =vnp.copy()  # same in embedded
										snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name.startswith('model/wob'			):
								snake_case__	     ='final_logits_bias'
								snake_case__	     =vnp.copy()  # same in embedded
								snake_case__	     =state.reshape((1, -1)			)
								snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name == "model/dense/kernel":
								snake_case__	     ='model.last_project.weight'
								snake_case__	     =vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
								snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name == "model/dense_1/bias":
								snake_case__	     ='model.last_project.bias'
								snake_case__	     =vnp.copy()  # same because it is one dimensional
								snake_case__	     =torch.tensor(UpperCamelCase_			)
		torch.save(UpperCamelCase_      ,					args.output			)


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE__     :						int	=  argparse.ArgumentParser(
			    description='''model converter.''', formatter_class=argparse.ArgumentDefaultsHelpFormatter
			)
			parser.add_argument('''--tf_model_dir''', metavar='''PATH''', type=str, required=True, help='''import model''')
			parser.add_argument('''--output''', metavar='''PATH''', type=str, required=True, help='''output model''')
			SCREAMING_SNAKE_CASE__     :						str	=  parser.parse_args()
			convert_tf_gptsan_to_pt(args)




